1. Field of the Invention
This invention relates to a moving body recognition apparatus. A moving body recognition apparatus in many cases uses a TV camera as an image input unit receiving an image of an external object.
2. Description of the Related Arts
Image processing devices are used widely, e.g. for an FA (factory automation) inspection, an automatic monitoring device, and a visual sensor for an automatically operated vehicle, generally as devices capable of processing visual information as with a human being. There are potential demands for them and their research and development is actively conducted. Although an image processing device originally processed a still image as its object, recently it has put a greater emphasis on processing a moving image. Especially, those capable of recognizing a moving body have come to take a large share of all image processing devices.
A moving body recognition apparatus generally recognizes the shape of a moving body and its relative movement, when the image input unit of the moving body recognition apparatus is moving in relation to the object. That is, even if an object is not actually moving, when an image input unit of a moving body recognition apparatus moves, the moving body recognition apparatus recognizes the shape of the object standing still and the movement of the image input unit. For example, in a case of a visual sensor for an automatically operated vehicle, its image input unit is loaded on top of the vehicle, and the moving body recognition apparatus recognizes the environment in which the vehicle is running.
A moving body recognition apparatus must be compact and responsive. Compactness is critical, especially when a moving body recognition apparatus is loaded on a vehicle in its application to a visual sensor of an automatically operated vehicle. Responsiveness is crucial, because a realtime processing similar to a human vision is required.
A conventional moving body recognition device captures an object by two image input units. By establishing the correspondences between the feature points of the objects in the two images captured by the two image input units, the shape of the object is captured at every certain instant in time for observation by applying a principle of a triangulation, and then the movement of the object is calculated.
FIG. 1 shows a concept of a conventional moving body recognition apparatus.
A first image input unit 2 and a second image input unit 3 input images of an object 1 to a moving image recognition unit 4. The moving body recognition unit 4 detects feature points of an object 1 from the two images. By matching a same feature point between the two images, the position of a feature point is calculated by applying a principle of a triangulation. Here, a feature point refers to a point representing a particular part of the object 1. When there is a peak, an outline point or a pattern, a feature point may be a dot in a pattern or on a color boundary. The moving body recognition unit 4 calculates the movement of a feature point and the object 1 from a shift of the feature point in a time series. The moving body recognition apparatus outputs as a recognition result 5 the position and movement of a feature point and the movement of an object.
FIG. 2 shows a configuration of a conventional moving body recognition apparatus.
A first feature point extraction unit 6 extracts a feature point from an image inputted by the first image input unit 2 and supplies it to a feature point correspondence unit 8. Likewise, a second feature point extraction unit 7 extracts a feature point from an image inputted by the second image input unit 3 and supplies it to the feature point correspondence unit 8. The feature point correspondence unit 8 matches the same feature points from among the feature points extracted from the first feature point extraction unit 6 and the second feature point extraction unit 7.
A feature point position calculation unit 9 obtains the positions of feature points by relating the positions of the matched feature points with the positions of the first image input unit 2 and the second image input unit 3, and stores the result in a feature point position storage unit 10. The positions of feature points at plural instants in time for observation stored in the feature point position storage unit 10 are sent to an object movement calculation unit 11, which calculates the movement of an object and stores the result in an object movement storage unit 12.
However, a conventional moving body recognition apparatus as explained in the description of FIGS. 1 and 2 have the following problems.
(a) Because two feature point extraction units need to individually extract feature points extracted from two images captured respectively by two image input units, the process load for extracting a feature point is twice as much as that by using a single TV camera. PA1 (b) An additional process of matching features from two images captured differently is required. The feature point correspondence unit 8 is required as shown in FIG. 2. Because the positions of two image input units are different, they capture the object 1 differently. This makes it difficult to match feature points of the object 1. Hence, the feature point correspondence unit 8 requires a large workload for searching for corresponding feature points. (The closer the position of two image input units, the easier it is to make correspondences between feature points of an object, but the less accurate a recognition of the shape of an object becomes.)
Typically, processing in (b) is impossible when a feature point of an object captured by one image input unit cannot be captured by the other, where no correspondence between those feature points can be made.
FIG. 3 shows an example in which a conventional moving body recognition apparatus fails to establish correspondences between feature points captured by different image input units.
The object 1 has two feature points, for instance. However, there is a case in which both the first image input unit 2 and the second image input unit 3 can capture only one of the two feature points.